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Multi-target tracking algorithm of boost-phase ballistic missile defense 被引量:2
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作者 Kangsheng Tian Feng Zhang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第1期90-100,共11页
Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking (MHT) concept is proposed. This paper ... Considering the problem of multiple ballistic missiles tracking of boost-phase ballistic missile defense, a boost-phase tracking algorithm based on multiple hypotheses tracking (MHT) concept is proposed. This paper focuses on the tracking algo- rithm for hypothesis generation, hypothesis probability calculation, hypotheses reduction and pruning and other sectors. From an engineering point of view, a technique called the linear assignment problem (LAP) used in the implementation of M-best feasible hypotheses generation, the number of the hypotheses is relatively small compared with the total number that may exist in each scan, also the N-scan back pruning is used, the algorithm's efficiency and practicality have been improved. Monte Carlo simulation results show that the proposed algorithm can track the boost phase of multiple ballistic missiles and it has a good tracking performance compared with joint probability data association (JPDA). 展开更多
关键词 ballistic missile multiple hypotheses tracking (MHT) linear assignment problem (LAP) hypothesis pruning.
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A NEW DATA ASSOCIATION ALGORITHM USING PROBABILITY HYPOTHESIS DENSITY FILTER 被引量:2
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作者 Huang Zhipei Sun Shuyan Wu Jiankang 《Journal of Electronics(China)》 2010年第2期218-223,共6页
Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,P... Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper,we proposed a new data association algorithm using GMPHD filter,which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime. 展开更多
关键词 Multi-target trajectory tracking Probability Hypothesis Density (PHD) Gaussian mixture ((]M) model multiple hypotheses detection Peak-to-track association
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